Proffler: Toward Collaborative and Scalable Edge-Assisted Crowdsourced Livecast
Bibliographic record
Abstract
In recent years, crowdsourced livecast has seen remarkable progress due to the interactivity and real-time nature, playing an essential role in multimedia applications in the post-epidemic era. Given the delay sensitivity, large viewing volumes, and heterogeneous viewing patterns, the traditional video streaming methods fail to provide the optimized quality of experience (QoE) for viewers using the minimum system cost over an edge-assisted service architecture. The emerging technology of mobile edge computing (MEC) offers a new perspective of reducing user latency and enhancing the quality of dispatched videos in a promising way. In this paper, we propose Proffler, an integrated framework that addresses this problem through effective stream caching at the network edge server. We first examine the underlying correlations in viewing patterns across different regions and propose a novel transformer-based algorithm, Chili-TF, that achieves accurate viewer request prediction, even for regions with insufficient data. We then design a scalable algorithm, U2VR, that achieves near-optimal video stream allocation as well as viewer scheduling. Extensive real-data-driven experiments further confirm that Proffler can achieve improvements of 20%-55% in average QoE compared to state-of-the-art solutions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".